A Seat at the Table is a 2.5-hour beginner-friendly AI workshop for eight women. You learn the safety basics, practise giving good instructions and complete one real task from your own to-do list.
What is a hands-on AI workshop?
A hands-on AI workshop teaches through supervised practice. Instead of watching a demonstration, every participant uses an AI assistant on her own laptop. The goal is not to understand every technical detail; it is to know what the tool can do, what not to share with it and how to get a useful result you can evaluate.
What you learn
How generative AI works in plain language
You learn enough to understand why an AI assistant can sound confident while being wrong, why your judgment still matters and which tasks are a good fit.
What never to paste into an AI tool
The session covers the practical safety boundary: confidential client information, personal data, passwords, unpublished financial information and anything you do not have permission to share.
How to give useful context and direction
Good prompting is closer to briefing a smart new colleague than typing keywords into a search box. You practise explaining the goal, audience, constraints and desired format, then improve the result through conversation.
How to judge the answer
AI output is a draft, not an authority. You learn to check facts, remove invented details, protect your own voice and decide whether the result is actually useful.
Examples of tasks participants can bring
- A difficult client or colleague email
- A proposal, agenda or project plan
- A job description or interview questions
- A marketing post or newsletter outline
- A policy, process or long document that needs simplifying
- A weekly plan, meal plan or other piece of life administration
Why a women-only room?
Research across more than 100 countries has found a gender gap in generative AI adoption. Workplace encouragement and the fear that using AI will be judged as cheating are part of that gap. A small women-only session creates room to experiment without needing to perform confidence first.
Read the evidence in our briefing on the AI confidence gap.